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MAC-Aware and Power-Aware Image Aggregation Scheme in Wireless Visual Sensor Networks

机译:无线视觉传感器网络中的MAC感知和Power感知图像聚合方案

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Traditional wireless sensor networks (WSNs) transmit the scalar data (e.g., temperature and irradiation) to the sink node. A new wireless visual sensor network (WVSN) that can transmit images data is a more promising solution than the WSN on sensing, detecting, and monitoring the environment to enhance awareness of the cyber, physical, and social contexts of our daily activities. However, the size of image data is much bigger than the scalar data that makes image transmission a challenging issue in battery-limited WVSN. In this paper, we study the energy efficient image aggregation scheme in WVSN. Image aggregation is a possible way to eliminate the redundant portions of the image captured by different data source nodes. Hence, transmission power could be reduced via the image aggregation scheme. However, image aggregation requires image processing that incurs node processing power. Besides the additional energy consumption from node processing, there is another MAC-aware retransmission energy loss from image aggregation. In this paper, we first propose the mathematical model to capture these three factors (image transmission, image processing, and MAC retransmission) in WVSN. Numerical results based on the mathematical model and real WVSN sensor node (i.e., Meerkats node) are performed to optimize the energy consumption tradeoff between image transmission, image processing, and MAC retransmission.
机译:传统的无线传感器网络(WSN)将标量数据(例如温度和辐射)传输到接收器节点。与WSN相比,一种新的可以传输图像数据的无线视觉传感器网络(WVSN)在感知,检测和监视环境方面具有更广阔的解决方案,以增强人们对我们日常活动的网络,物理和社交环境的意识。但是,图像数据的大小比标量数据大得多,这使得图像传输成为受电池限制的WVSN的难题。在本文中,我们研究了WVSN中的节能图像聚合方案。图像聚合是消除不同数据源节点捕获的图像的冗余部分的一种可能方法。因此,可以经由图像聚合方案来降低传输功率。但是,图像聚合要求图像处理,这会增加节点处理能力。除了来自节点处理的额外能耗之外,还有来自图像聚合的另一种MAC感知重传能量损耗。在本文中,我们首先提出一种数学模型来捕获WVSN中的这三个因素(图像传输,图像处理和MAC重传)。执行基于数学模型和实际WVSN传感器节点(即Meerkats节点)的数值结果,以优化图像传输,图像处理和MAC重传之间的能耗权衡。

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